Matthew King-smith

Senior Robotics Scientist at Titan Robotics, Inc.

Atlanta, Georgia, United States
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Summary

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Matthew King-smith is a Senior Robotics Scientist with 11 years of experience applying dynamic multibody modeling and real-time control to spacecraft and robotic manipulators, currently leading advanced work at Titan Robotics in Atlanta. He blends deep academic research—from a PhD in Robotics at Georgia Tech and collaborations with NASA JPL and Aerospace Corporation—with hands-on systems integration, including Speedgoat/Simulink real-time targets, SE(2) air-bearing platforms, and dual-quaternion modeling of multi-body systems. His background spans both algorithmic work (factor-graph optimization, sensor fusion, feedback linearization) and practical implementation in C++, ROS, and control allocation for novel actuators. Unusually, he pairs aerospace-grade research with early-career operational roles (yard foreman, sales/delivery), giving him pragmatic systems-thinking and logistics experience that informs robust experimental hardware development.
code11 years of coding experience
job2 years of employment as a software developer
bookDoctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at Georgia Institute of Technology
bookBachelor of Arts (B.A.), Physics, (Minor) Mathematics, Bachelor of Arts (B.A.), Physics, (Minor) Mathematics at The College of Wooster
languagesEnglish
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Github Skills (12)

robotics10
gtsam10
cpp10
perception10
gaussian-processes7
trajectory-optimization6
motion-planning6
inference3
deep-learning2
computer-vision2
pytorch2
matlab1

Programming languages (3)

C++CSSJupyter Notebook

Github contributions (5)

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mattking-smith/gpmp2-Borglab

Nov 2022 - May 2026

Gaussian Process Motion Planner 2 - Borglab Repository
Contributions:2 PRs, 35 pushes, 11 branches in 3 years 6 months
motiongaussianplannermotion-plannergaussian-process
mattking-smith/gtsam

Dec 2021 - Nov 2022

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Contributions:4 pushes in 11 months
smoothingsparsehierarchicalc-plus-plusgtsam
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